HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Driving

TL;DR

HUGSIM is a real-time, photo-realistic closed-loop simulator for autonomous driving that enhances evaluation and fine-tuning of algorithms through high-quality rendering and comprehensive scenario benchmarking.

Contribution

It introduces a novel 3D Gaussian Splatting-based rendering method for closed-loop scenarios and provides a large-scale, realistic benchmark dataset for autonomous driving evaluation.

Findings

High-quality, real-time rendering of autonomous driving scenes

Effective closed-loop simulation with dynamic environment updates

Benchmark dataset covering diverse scenarios from multiple sources

Abstract

In the past few decades, autonomous driving algorithms have made significant progress in perception, planning, and control. However, evaluating individual components does not fully reflect the performance of entire systems, highlighting the need for more holistic assessment methods. This motivates the development of HUGSIM, a closed-loop, photo-realistic, and real-time simulator for evaluating autonomous driving algorithms. We achieve this by lifting captured 2D RGB images into the 3D space via 3D Gaussian Splatting, improving the rendering quality for closed-loop scenarios, and building the closed-loop environment. In terms of rendering, We tackle challenges of novel view synthesis in closed-loop scenarios, including viewpoint extrapolation and 360-degree vehicle rendering. Beyond novel view synthesis, HUGSIM further enables the full closed simulation loop, dynamically updating the ego and actor states and observations based on control commands. Moreover, HUGSIM offers a comprehensive benchmark across more than 70 sequences from KITTI-360, Waymo, nuScenes, and PandaSet, along with over 400 varying scenarios, providing a fair and realistic evaluation platform for existing autonomous driving algorithms. HUGSIM not only serves as an intuitive evaluation benchmark but also unlocks the potential for fine-tuning autonomous driving algorithms in a photorealistic closed-loop setting.